Table 2.

Average results obtained by developed models on the deep features

Deep features/deep learning model		Acc (%)	Sen (%)	Spe (%)	Pre (%)
VGG-16	DNN	91.84 ± 2.23	69.6 ± 13.05	97.4 ± 2.8	89.21 ± 9.51
	Bi-LSTM	95.68 ± 1.93	88.0 ± 7.16	97.6 ± 2.33	91.02 ± 7.31
ResNet-50	DNN	92.64 ± 1.38	70.4 ± 8.24	98.2 ± 0.75	90.93 ± 3.02
	Bi-LSTM	93.28 ± 1.09	76.0 ± 5.66	97.6 ± 1.85	89.58 ± 6.28
DenseNet-121	DNN	88.32 ± 0.64	50.4 ± 6.5	98.0 ± 2.1	88.89 ± 10.69
	Bi-LSTM	89.76 ± 1.48	60.0 ± 8.0	97.4 ± 1.5	86.27 ± 8.02
Concatenated deep features	DNN	95.84 ± 1.06	82.4 ± 7.42	99.2 ± 0.75	96.59 ± 3.14
	Bi-LSTM	97.6 ± 0.88	91.2 ± 1.6	99.2 ± 1.17	96.86 ± 4.5

The best result is shown in bold font